The present work is a sequel to a recent one published on this journal where the superiority of `radial design' to compute the `total
sensitivity index' was ascertained. Both concepts belong to sensitivity analysis of model output. A radial design is the one whereby starting
from a random point in the hyperspace of the input factors one step in turn is taken for each factor. The procedure is iterated a number
of times with a different starting random point as to collect a sample of elementary shifts for each factor. The total sensitivity index is a
powerful sensitivity measure which can be estimated based on such a sample. Given the similarity between the total sensitivity index and a
screening test known as method of the elementary effects (or method of Morris), we test the radial design on this method. Both methods
are best practices: the total sensitivity index in the class of the quantitative measures and the elementary effects in that of the screening
methods. We ¯nd that the radial design is indeed superior even for the computation of the elementary effects method. This opens the door to a sensitivity analysis strategy whereby the analyst can start with a small number of points (screening-wise) and then { depending
on the results { possibly increase the numeral of points up to compute a fully quantitative measure. Also of interest to practitioners is
that a radial design is nothing else than an iterated `One factor At a Time' (OAT) approach. OAT is a radial design of size one. While
OAT is not a good practice, modelers in all domains keep using it for sensitivity analysis for reasons discussed elsewhere. With the
present approach modelers are offered a straightforward and economic upgrade of their OAT which maintain OAT's appeal of having just one factor moved at each step.